Neural Embedding Allocation: Distributed Representations of Topic Models
We propose a method that uses neural embeddings to improve the performance of any given LDA-style topic model. Our method, called neural embedding allocation (NEA), deconstructs topic models (LDA or otherwise) into interpretable vector-space embeddings of words, topics, documents, authors, and so on...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
The MIT Press
2022-08-01
|
| Series: | Computational Linguistics |
| Online Access: | http://dx.doi.org/10.1162/coli_a_00457 |